Multi-rate adaptive inferential control

The measurement of key process outputs at a rate suitable for online control is a problem common to many processes. Either the instrumentation does not exist or analysers with long cycle times are used. This paper reports on a natural development of the inferential estimation technique. Instead of merely providing estimates of the primary output, the model used for inferential estimation is used to develop a multirate adaptive inferential controller (AIC) based upon the generalised minimum variance (GMV) control philosophy. The resulting algorithm was evaluated by application to a nonlinear simulation of a binary distillation column. It is shown that the technique can provide significantly enhanced performance when compared to alternate model based control design techniques.